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Dendritic cells (DCs) sense environmental cues and adopt either an immune-stimulatory or regulatory phenotype, thereby fine-tuning immune responses. Identifying endogenous regulators that determine DC function can thus inform the development of therapeutic strategies for modulating the immune response in different disease contexts. Tim-3 plays an important role in regulating immune responses by inhibiting the activation status and the T cell priming ability of DC in the setting of cancer. Bat3 is an adaptor protein that binds to the tail of Tim-3; therefore, we studied its role in regulating the functional status of DCs. In murine models of autoimmunity (experimental autoimmune encephalomyelitis) and cancer (MC38-OVA–implanted tumor), lack of Bat3 expression in DCs alters the T cell compartment—it decreases TH1, TH17 and cytotoxic effector cells, increases regulatory T cells, and exhausted CD8+ tumor-infiltrating lymphocytes, resulting in the attenuation of autoimmunity and acceleration of tumor growth. We found that Bat3 expression levels were differentially regulated by activating versus inhibitory stimuli in DCs, indicating a role for Bat3 in the functional calibration of DC phenotypes. Mechanistically, loss of Bat3 in DCs led to hyperactive unfolded protein response and redirected acetyl–coenzyme A to increase cell intrinsic steroidogenesis. The enhanced steroidogenesis in Bat3-deficient DC suppressed T cell response in a paracrine manner. Our findings identified Bat3 as an endogenous regulator of DC function, which has implications for DC-based immunotherapies. [ABSTRACT FROM AUTHOR]

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles. Author summary: Recently, several statistical methods have been proposed to identify phenotypic or environmental associations with features (e.g., taxa, genes, pathways, chemicals, etc.) from molecular profiles of microbial communities. Particularly for human microbiome epidemiology, however, most of these are primarily focused on univariable associations that analyze only one or a few environmental covariates. This is a critical gap to address, given the growing commonality of population-scale microbiome research and the complexity of associated study designs, including dietary, pharmaceutical, clinical, and environmental covariates, often with samples from multiple time points or tissues. Surprisingly, there have been no systematic evaluations of statistical analysis methods appropriate for such studies, nor consensus on appropriate methods for scalable microbiome epidemiology. To this end, we developed and validated a statistical model (MaAsLin) that provides both the first unified method and the first large-scale, comprehensive benchmarking of multivariable associations in population-scale microbial community studies. We hope that the MaAsLin 2 implementation, validated through extensive simulations and an application to HMP2 IBD multi-omics, will be helpful for researchers in future analysis of both human-associated and environmental microbial communities. [ABSTRACT FROM AUTHOR]

Background: Microglia, the resident brain immune cells, play a critical role in brain homeostasis and disease progression. In neurodegenerative conditions, microglia acquire the neurodegenerative phenotype (MGnD), whose function is poorly understood. MicroRNA‐155 (miR‐155), enriched in immune cells, critically regulates MGnD. However, its role in Alzheimer's disease (AD) pathogenesis remains unclear. Methods: We used RNAseq and immunohistochemistry (n = 6‐8 per sex per group) to investigate the gene expression profile and AD pathology. We further utilized single‐cell RNAseq (n = 5 per group) to identify microglial clusters. Whole‐tissue proteomics (n = 4) was applied to detect the protein changes in brain milieu. Moreover, we used spontaneous alternation and forced alternation tests to evaluate the cognition (n = 33‐36). Result: We report that microglial deletion of miR‐155 induces a pre‐MGnD activation state via interferon‐g (IFNg) signaling and blocking IFNg signaling attenuates MGnD induction and microglial phagocytosis. Single‐cell RNAseq analysis of microglia from AD mouse model identifies Stat1 and Clec2d as pre‐MGnD markers. This phenotypic transition enhances amyloid plaque compaction, reduces dystrophic neurites, attenuates plaque‐associated synaptic degradation, and improves cognition. Conclusion: Our study demonstrates a novel miR‐155‐mediated regulatory mechanism of MGnD and the beneficial role of IFNg‐responsive pre‐MGnD in restricting neurodegenerative pathology and preserving cognitive function in an AD mouse model, highlighting miR‐155 and IFNg as potential therapeutic targets for AD. [ABSTRACT FROM AUTHOR]

A correction to the article "Reference-free inference of tumor phylogenies from single-cell sequencing data," by Ayshwarya Subramanian and Russell Schwartz that was published in the 2015 issue is presented.

Background: Effective management and treatment of cancer continues to be complicated by the rapid evolution and resulting heterogeneity of tumors. Phylogenetic study of cell populations in single tumors provides a way to delineate intra-tumoral heterogeneity and identify robust features of evolutionary processes. The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially with regard to chromosome abnormalities that typically dominate tumor evolution. Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression. Results: We propose a reference-free approach to mining single-cell genome sequence reads to allow predictive classification of tumors into heterogeneous cell types and reconstruct models of their evolution. The approach extracts k-mer counts from single-cell tumor genomic DNA sequences, and uses differences in normalized k-mer frequencies as a proxy for overall evolutionary distance between distinct cells. The approach computationally simplifies deriving phylogenetic markers, which normally relies on first aligning sequence reads to a reference genome and then processing the data to extract meaningful progression markers for constructing phylogenetic trees. The approach also provides a way to bypass some of the challenges that massive genome rearrangement typical of tumor genomes presents for reference-based methods. We illustrate the method on a publicly available breast tumor single-cell sequencing dataset. Conclusions: We have demonstrated a computational approach for learning tumor progression from single cell sequencing data using k-mer counts. k-mer features classify tumor cells by stage of progression with high accuracy. Phylogenies built from these k-mer spectrum distance matrices yield splits that are statistically significant when tested for their ability to partition cells at different stages of cancer. [ABSTRACT FROM AUTHOR]

Computational cancer phylogenetics seeks to enumerate the temporal sequences of aberrations in tumor evolution, thereby delineating the evolution of possible tumor progression pathways, molecular subtypes, and mechanisms of action. We previously developed a pipeline for constructing phylogenies describing evolution between major recurring cell types computationally inferred from whole-genome tumor profiles. The accuracy and detail of the phylogenies, however, depend on the identification of accurate, high-resolution molecular markers of progression, i.e., reproducible regions of aberration that robustly differentiate different subtypes and stages of progression. Here, we present a novel hidden Markov model (HMM) scheme for the problem of inferring such phylogenetically significant markers through joint segmentation and calling of multisample tumor data. Our method classifies sets of genome-wide DNA copy number measurements into a partitioning of samples into normal (diploid) or amplified at each probe. It differs from other similar HMM methods in its design specifically for the needs of tumor phylogenetics, by seeking to identify robust markers of progression conserved across a set of copy number profiles. We show an analysis of our method in comparison to other methods on both synthetic and real tumor data, which confirms its effectiveness for tumor phylogeny inference and suggests avenues for future advances. [ABSTRACT FROM PUBLISHER]

Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or 'progression pathways,' seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling thosemarkers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers. [ABSTRACT FROM AUTHOR]

Purpose : Human leukocyte antigen (HLA) mediates interactions of tumor cells with cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. Retinoblastoma (RB) is the most common intraocular malignant tumor in childhood and is characterized by direct spread to the optic nerve and orbit as well as hematogenous and lymphatic spread. Earlier, we observed that invasive RB showed reduced HLA, which could contribute to its escape from the immune system. Little is known about the role of the nonclassical HLA molecule, HLA-G, in RB and its role in tumor escape mechanisms in RB. Methods : Forty archival paraffin-embedded RB tumors were analyzed for the expression of HLA-G by immunohistochemistry using a monoclonal antibody; fresh tumor samples were also subjected to Western blot analysis. There were 22 tumors with no invasion and 18 with invasion of the choroid/optic nerve. Immunoanalysis was performed based on the International Histocompatibility Working Group Project Description. Results : HLA-G was negative in the non-neoplastic retina, reduced in 22/22 tumors with no invasion, and positive in 15/18 with invasion. The immunohistochemistry results were confirmed by Western blot analysis. The difference in expression between the two groups was significant ( p [ABSTRACT FROM AUTHOR]

Motivation: Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data. [ABSTRACT FROM PUBLISHER]

B cells can express pro-inflammatory cytokines that promote a wide variety of immune responses. Here we show that B cells expressing the phosphatidylserine receptor TIM-4, preferentially express IL-17A, as well as IL-22, IL-6, IL-1β, and GM-CSF - a collection of cytokines reminiscent of pathogenic Th17 cells. Expression of this proinflammatory module requires IL-23R signaling and selective expression of RORγt and IL-17A by TIM-4 + B cells. TIM-4 + B cell-derived-IL-17A not only enhances the severity of experimental autoimmune encephalomyelitis (EAE) and promotes allograft rejection, but also acts in an autocrine manner to prevent their conversion into IL-10-expressing B cells with regulatory function. Thus, IL-17A acts as an inflammatory mediator and also enforces the proinflammatory activity of TIM-4 + B cells. Thus, TIM-4 serves as a broad marker for RORγt + effector B cells (Beff) and allows further study of the signals regulating Beff differentiation and effector molecule expression.

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods., Competing Interests: AG serves as a founding advisor to Goldfinch Biopharma and to a new Atlas Ventures funded company, with respective agreements reviewed and managed by Mass General Brigham (MGB) and the Broad Institute of MIT and Harvard in accordance with their conflict of interest policies. RRS, FC, and EZM are inventors on a pending patent application related to the development of Slide-seq. EZM is an advisor to Curio Biosciences, Inc. FC is a paid consultant for Atlas Bio. KAV is currently an employee and shareholder of Q32 Bio, Inc. JLM is currently an employee and shareholder of Solid Biosciences, Inc., (© 2022 The Author(s).)

Dendritic cells (DCs) sense environmental cues and adopt either an immune-stimulatory or regulatory phenotype, thereby fine-tuning immune responses. Identifying endogenous regulators that determine DC function can thus inform the development of therapeutic strategies for modulating the immune response in different disease contexts. Tim-3 plays an important role in regulating immune responses by inhibiting the activation status and the T cell priming ability of DC in the setting of cancer. Bat3 is an adaptor protein that binds to the tail of Tim-3; therefore, we studied its role in regulating the functional status of DCs. In murine models of autoimmunity (experimental autoimmune encephalomyelitis) and cancer (MC38-OVA-implanted tumor), lack of Bat3 expression in DCs alters the T cell compartment-it decreases T H 1, T H 17 and cytotoxic effector cells, increases regulatory T cells, and exhausted CD8 + tumor-infiltrating lymphocytes, resulting in the attenuation of autoimmunity and acceleration of tumor growth. We found that Bat3 expression levels were differentially regulated by activating versus inhibitory stimuli in DCs, indicating a role for Bat3 in the functional calibration of DC phenotypes. Mechanistically, loss of Bat3 in DCs led to hyperactive unfolded protein response and redirected acetyl-coenzyme A to increase cell intrinsic steroidogenesis. The enhanced steroidogenesis in Bat3-deficient DC suppressed T cell response in a paracrine manner. Our findings identified Bat3 as an endogenous regulator of DC function, which has implications for DC-based immunotherapies.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients' demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63 + intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies., Competing Interests: Competing Interests P.D., R.F., E.M.M., M.R., E.H.R., L.P., T.He., J.R., J.B., and S.W. are employees and stockholders at Nanostring Technologies Inc. D.Z., is a former employee and stockholder at NanoString Technologies. N.H., holds equity in BioNTech and Related Sciences. T.H.is an employee and stockholder of Prime Medicine as of Oct. 13, 2020. G.H. is an employee of Genentech as of Nov 16, 2020. R.N. is a founder, shareholder, and member of the board at Rhinostics Inc. A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until July 31, 2020. From August 1, 2020, A.R. is an employee of Genentech. From October 19, 2020, O.R.-R is an employee of Genentech. P.C.S is a co-founder and shareholder of Sherlock Biosciences, and a Board member and shareholder of Danaher Corporation. A.K.S. reports compensation for consulting and/or SAB membership from Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Ochre Bio, and Dahlia Biosciences. Z.G.J. reports grant support from Gilead Science, Pfizer, compensation for consulting from Olix Pharmaceuticals. Y.V.P. reports grant support from Enanta Pharmaceuticals, CymaBay Therapeutics, Morphic Therapeutic; consulting and/or SAB in Ambys Medicines, Morphic Therapeutics, Enveda Therapeutics, BridgeBio Pharma, as well as being an Editor – American Journal of Physiology-Gastrointestinal and Liver Physiology. GS reports consultant service in Alnylam Pharmaceuticals, Merck, Generon, Glympse Bio, Inc., Mayday Foundation, Novartis Pharmaceuticals, Quest Diagnostics, Surrozen, Terra Firma, Zomagen Bioscience, Pandion Therapeutics, Inc. Durect Corporation; royalty from UpToDate Inc., and Editor service in Hepatology Communications. P.R.T. receives consulting fees from Cellarity Inc., and Surrozen Inc., for work not related to this manuscript.

Drug repurposing is the only method capable of delivering treatments on the shortened time-scale required for patients afflicted with lung disease arising from SARS-CoV-2 infection. Mucin-1 (MUC1), a membrane-bound molecule expressed on the apical surfaces of most mucosal epithelial cells, is a biochemical marker whose elevated levels predict the development of acute lung injury (ALI) and respiratory distress syndrome (ARDS), and correlate with poor clinical outcomes. In response to the pandemic spread of SARS-CoV-2, we took advantage of a high content screen of 3,713 compounds at different stages of clinical development to identify FDA-approved compounds that reduce MUC1 protein abundance. Our screen identified Fostamatinib (R788), an inhibitor of spleen tyrosine kinase (SYK) approved for the treatment of chronic immune thrombocytopenia, as a repurposing candidate for the treatment of ALI. In vivo , Fostamatinib reduced MUC1 abundance in lung epithelial cells in a mouse model of ALI. In vitro , SYK inhibition by Fostamatinib promoted MUC1 removal from the cell surface. Our work reveals Fostamatinib as a repurposing drug candidate for ALI and provides the rationale for rapidly standing up clinical trials to test Fostamatinib efficacy in patients with COVID-19 lung injury.